Image segmentation is the process of dividing a digital image into meaningful regions, and is generally the first step in automated image analysis. The objects of interest are extracted from the image for subsequent processing, such as description and recognition. In general, segmentation is one of the most important and most difficult steps in automated image analysis. The success or failure of the image analysis task is often a direct consequence of the success or failure of the segmentation.
Nuclear image analysis is one of the main application fields of automated image analysis and is a useful method to obtain quantitative information for the diagnosis and prognosis of human cancer. In segmentation of cell nuclei the complexity of the problem largely depends on the type of specimen. Nuclei in cytological specimens may be segmented by simple automatic gray level thresholding. Nuclei in tissue sections, however, are in general very difficult to segment by purely automatic means. It may be necessary to use interactive techniques in order to obtain sufficient quality as discussed in E. Bengtsson, C. Wahlby and J. Lindblad, “Robust cell image segmentation methods”, Pattern Recognition and Image Analysis 14 (2004), 157-167. Bengtsson et al. discussed the relative advantages of different approaches to cell segmentation.
Active contour models or snakes are widely used in medical image segmentation. See M. Kass, A. Witkin and D. Terzopoulos, “Snakes—Active contour models”, Int J Comput Vision 1 (1988), 321-331 and T. Mclmemey and D. Terzopoulos, “Deformable models in medical image analysis: a survey”, Medical Image Analysis 1 (1996), 91-108.
Active contours were originally designed as interactive models and the idea behind active contours for image segmentation is quite simple; 1) the user specifies an initial guess for the contour, 2) the contour is then moved by image driven forces to the boundary of the object. However, one general problem related to these algorithms is that the initial contour needs to be relatively close to the target object boundary in order to get convergence. This is known as the “capture range problem”.
Xu and Prince suggested a method for increasing the capture range of the external gradient vector field, in C. Xu and J. L. Prince, “Gradient Vector Flow (GVF) Active Contour Toolbox”, down-loaded from: http://iacl.ece.jhu.edu/projects/gvf and C. Xu and J. L. Prince, “Snakes, shapes, and gradient vector flow”, IEEE Trans Imag Proc 7 (1998), 359-369.
Trier and Taxt and Trier and Jain evaluated eleven locally adaptive thresholding methods on gray scale document images with low contrast, variable background intensity and noise. See Ø.D. Trier and T. Taxt, “Evaluation of binarization methods for document images”, IEEE trans on Pattern Analysis and Machine Intelligence 17 (1995), 312-315, and Ø.D. Trier and A. K. Jain, “Goal-directed evaluation of binarization methods”, IEEE Trans on Pattern Analysis and Machine Intelligence 17 (1995), 1191-1201.